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The Role of Analytics in Modern Organizations

Analytics has become the backbone of every large organization, essential for fostering growth. By reducing operational costs, speeding up responses, and promoting agility in crucial business strategies, analytics tools and solutions help create long-term business impact. Integrating analytics into operations streamlines processes, increases efficiency through standardization, and promotes the adoption of best practices.

Building Effective Analytics Teams

With the rise in data availability, there’s a renewed focus on building data analytics teams that deliver results. Many companies invest in analytics technology but overlook the importance of creating a strong data, research, and insights team. A centralized or hybrid operating model, involving external partners, hinges on factors like cost, resources, skills, and time to value. Centralized models offer control and long-term value, while hybrid models provide flexibility, faster implementation, and access to diverse expertise, balancing internal capabilities with external efficiencies for strategic alignment and risk management.

Overcoming Siloed Operations

Often, organizations work in silos on smaller projects, which can add value but risk losing long-term impact. Adopting a product-centric mindset, and ensuring collaboration between global and local units can avoid duplication and enable scalability. A product-centric mindset in analytics involves creating multidisciplinary teams (product owners, data scientists, data engineers, business analysts, UX/UI designers) to develop analytics products that meet stakeholder needs. This approach prioritizes stakeholder feedback, continuous improvement, and cross-functional collaboration.

Data-Driven Investment Decisions

Investment and valuation decisions are often made from a local perspective, which can vary greatly based on the maturity of the business unit. Over time, analytics can provide a more structured approach, using frameworks like the DVF Framework (Desirability- Viability- Feasibility) to guide investment decisions and measure delivered value through standard measurement methods.

The DVF framework prioritizes projects by evaluating three key factors. Desirability ensures that the project aligns with customer needs and preferences, while Viability confirms the financial and strategic benefits. Feasibility assesses the technical and operational capability to execute the project. By thoroughly evaluating these factors across various use cases or opportunities, organizations can effectively select high-impact, achievable projects that drive meaningful results.

Leveraging External Data

While internal data is critical, organizations increasingly need to process and analyze external data from sources like social media, blogs, image and video sites, and sensors. For instance, a machine learning algorithm, targets campaigns to specific consumer segments by analyzing campaign and consumer data to find the optimal audience for a given campaign. Another example is Portfolio Optimization for customers through benchmarking of high-performing product portfolios using sell-out data and historical net revenue. Recommendations should then be implemented through existing value delivery platforms. Businesses must draw insights from customer data, analyze it, and offer predictions to test hypotheses and evaluate outcomes that can be put into practice.

Fostering Talent in Analytics and Democratizing AI

Integrating analytics into core business functions across various verticals can ignite growth in the analytics domain and create career paths and incentives to retain top talent. Analytics solutions combine technology and human input to refine results further. The future lies in democratizing AI, creating applications that enable business verticals to use analytics effectively and become an extension of the analytics community.

Ensuring Effective Integration and Governance

To ensure a successful integration of analytics tools, businesses need to expand analytical capabilities, roles, and processes. This includes anticipating changes in products and practices, planning for platform convergence, and fostering collaboration between data and analytics teams across the organization. Simply gathering data and using visualization tools is just the beginning.

Conclusion: Recommendations for Organizations

In conclusion, to fully harness the power of analytics, organizations should prioritize building strong, centralized analytics teams and integrate data-driven decision-making into their core operations. This includes fostering collaboration between global and local teams, adopting a product-centric approach, and leveraging both internal and external data sources. Investing in advanced technologies like machine learning models and creating scalable frameworks for measuring value will ensure long-term impact. By democratizing AI and expanding analytical capabilities, companies can streamline processes, enhance efficiency, and maintain a competitive edge in the market. Establishing a robust data management infrastructure and nurturing a data-driven culture are crucial steps toward sustained growth and innovation.

Article written during my tenure as Director Analytics at ABInBev, Growth Analytics Center.